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- Tasks: Join us to build innovative AI solutions that enhance children's learning experiences.
- Company: Explore Learning is a top educational organisation dedicated to making learning fun and effective.
- Benefits: Enjoy remote work, flexible holidays, gym discounts, and enhanced parental leave.
- Why this job: Be part of a fast-paced team transforming education with cutting-edge AI technology.
- Qualifications: 3+ years as a full-stack ML engineer with end-to-end ML pipeline experience required.
- Other info: Work remotely with occasional UK travel and enjoy 27 days of flexible annual leave.
The predicted salary is between 43200 - 72000 £ per year.
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Explore Learning is a leading educational organisation that is committed to making learning enjoyable and effective for children. With our network of learning centres across the country and our online tutor offering, we help thousands of children develop their skills and reach their full potential.
Join us as a full-stack AI engineer building cutting-edge educational solutions from data pipeline to production. You\’ll own the complete ML lifecycle – from raw data engineering through to deploying conversational AI that transforms how parents and educators understand student learning. Report to Head of Machine Learning in a fast-paced, innovative environment.
The Role
Key Responsibilities
End-to-end AI development
- Build complete AI solutions: from data ingestion to deployed GenAI applications
- Create scalable data pipelines processing millions of educational records
- Develop advanced prompt engineering for personalised, encouraging insights
- Architect data pipelines with Azure Data Factory and transform raw data into ML-ready features
- Implement cutting-edge pre-computation strategies for real-time AI responses
- Co-own the entire solution with the ML Team: data engineering → model development → production deployment and maintenance.
- Drive innovation across the complete ML stack
Technical Leadership
- Partner with Backend/Frontend teams as the AI solution owner
- Collaborate with MLOps to ensure seamless model deployment
- Shape architectural decisions across the data-to-AI pipeline
Requirements
- 3+ years as full-stack ML engineer (data engineering + AI development)
- End-to-end ML pipeline experience – not just models, but complete solutions
- UK work authorisation
- Real-time ML systems
- Published ML projects
Logistics
Location:Remote, with occasional UK travel
Reports to: Head of Machine Learning
Contract: Full time, permanent contract
Holidays: 27 days of flexible annual leave. In addition to this, we close for sessions from 24th-1st January every year and don\’t require you to take any annual leave during this time.Additional 3 paid days you can take for a personal or religious celebration, wellbeing or a volunteer day.
Wellbeing: Discounted gym memberships and sportwear, free counselling, cycle to work scheme and a discounted tastecard.
Benefits: Referral bonus, staff discounts, enhanced parental leave and pension.
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Full-stack AI Engineer employer: Explore Learning
Contact Detail:
Explore Learning Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Full-stack AI Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in AI and machine learning, especially in the education sector. Being able to discuss recent advancements or case studies during your interview can demonstrate your passion and knowledge about the field.
✨Tip Number 2
Showcase your experience with end-to-end ML pipelines by preparing examples of past projects where you’ve handled everything from data ingestion to deployment. This will help you illustrate your comprehensive skill set and how it aligns with the role.
✨Tip Number 3
Network with professionals in the AI and education sectors. Engaging with communities on platforms like LinkedIn or attending relevant meetups can provide insights into the company culture and potentially lead to referrals.
✨Tip Number 4
Prepare to discuss your collaborative experiences with cross-functional teams, particularly in backend and frontend development. Highlighting your ability to work well with others will be crucial, as this role involves partnering with various teams.
We think you need these skills to ace Full-stack AI Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience as a full-stack ML engineer. Focus on your end-to-end ML pipeline experience and any published ML projects that demonstrate your skills in data engineering and AI development.
Craft a Compelling Cover Letter: In your cover letter, express your passion for educational technology and how your skills align with the role. Mention specific experiences where you've built AI solutions or collaborated with teams to drive innovation.
Showcase Relevant Projects: If you have any relevant projects, especially those involving real-time ML systems or Azure Data Factory, be sure to include them in your application. This will help demonstrate your technical leadership and problem-solving abilities.
Highlight Soft Skills: Since the role involves collaboration with various teams, emphasise your communication and teamwork skills. Mention any experiences where you've successfully partnered with backend/frontend teams or MLOps to ensure seamless model deployment.
How to prepare for a job interview at Explore Learning
✨Showcase Your Full-Stack Experience
Make sure to highlight your experience in both data engineering and AI development. Discuss specific projects where you’ve built complete ML solutions, as this role requires a strong understanding of the entire ML lifecycle.
✨Demonstrate Technical Leadership
Prepare examples of how you've led technical initiatives or collaborated with cross-functional teams. This role involves partnering with Backend and Frontend teams, so showing your ability to lead and communicate effectively is key.
✨Familiarise Yourself with Azure Data Factory
Since the job involves architecting data pipelines with Azure Data Factory, brush up on your knowledge of this tool. Be ready to discuss how you’ve used it in past projects or how you would approach building scalable data pipelines.
✨Prepare for Real-Time ML Discussions
Given the focus on real-time AI responses, be prepared to discuss your experience with real-time ML systems. Think about challenges you've faced and how you overcame them, as well as any innovative strategies you've implemented.